DNA cytosine methylation is the addition of a methyl group to a cytosine in the DNA. It impacts transcription and therefore plays a major role in several vital processes. In mammals, DNA cytosine methylation predominantly occurs in CG sequence contexts. In plants, in addition to the CG context, the CHG and CHH contexts are common as well.
Genomic regions, which control gene expression and protein production, are influenced by DNA methylation. Methylation can modify how these regions function, often silencing gene expression or regulating gene activity, thus playing a important role in development and genome stability.
Jensen-Shannon Divergence (JSD) measures the similarity between probability distributions, providing a symmetric comparison of datasets. In DNA methylation analysis, JSD can be used to compare methylation patterns across samples, identifying regions with significant epigenetic differences.
The goal of smoothing in DNA methylation analysis is to lower mistakes in divergence computations so that methylation patterns can be compared more accurately. This method contributes to sample consistency, which yields more reliable results for analysis such as JSD.
The datasets used in this study are from the [ 1001 Arabidopsis Epigenomes Project] (http://signal.salk.edu/1001.php)
This pipeline supports DNA methylation analysis by utilizing divergence calculations and generating outputs that can be used for additional statistical testing or visualization for further analysis.We used the `Methylator´ Framework for the data preparetion.
We analyzed the impact of different temperature conditions (10°C, 16°C, 22°C) on DNA methylation in the CHG, CHH, and CpG contexts across various genomic regions. By calculating the JSD and methylation levels, we revealed distinct patterns of methylation context-specific changes across these temperature conditions.
We further explored how methylation levels and JSD vary across different genomic regions, such as exonic, intergenic, and transposable elements, under three temperature conditions (10°C, 16°C, 22°C). This analysis aimed to identify regions that are particularly sensitive to temperature-induced epigenetic changes. The results could provide a clearer image of how genomic context influences methylation patterns and gene regulation in response to environmental stress.
Different gene expression profiles among samples at different temperatures (10°C, 16°C, and 22°C) are displayed in this transcriptome heatmap. The clustering shows that there are discernible variations in the effects of temperature on gene expression. The precise genes at play and their possible functions in temperature adaptation and stress responses in plants require more research.